Classification and characterization of human endogenous retroviruses; mosaic forms are common.

BACKGROUND: Human endogenous retroviruses (HERVs) represent the inheritance of ancient germ-line cell infections by exogenous retroviruses and the subsequent transmission of the integrated proviruses to the descendants. ERVs have the same internal structure as exogenous retroviruses. While no replication-competent HERVs have been recognized, some retain up to three of four intact ORFs. HERVs have been classified before, with varying scope and depth, notably in the RepBase/RepeatMasker system. However, existing classifications are bewildering. There is a need for a systematic, unifying and simple classification. We strived for a classification which is traceable to previous classifications and which encompasses HERV variation within a limited number of clades. RESULTS: The human genome assembly GRCh 37/hg19 was analyzed with RetroTector, which primarily detects relatively complete Class I and II proviruses. A total of 3173 HERV sequences were identified. The structure of and relations between these proviruses was resolved through a multi-step classification procedure that involved a novel type of similarity image analysis ("Simage") which allowed discrimination of heterogeneous (noncanonical) from homogeneous (canonical) HERVs. Of the 3173 HERVs, 1214 were canonical and segregated into 39 canonical clades (groups), belonging to class I (Gamma- and Epsilon-like), II (Beta-like) and III (Spuma-like). The groups were chosen based on (1) sequence (nucleotide and Pol amino acid), similarity, (2) degree of fit to previously published clades, often from RepBase, and (3) taxonomic markers. The groups fell into 11 supergroups. The 1959 noncanonical HERVs contained 31 additional, less well-defined groups. Simage analysis revealed several types of mosaicism, notably recombination and secondary integration. By comparing flanking sequences, LTRs and completeness of gene structure, we deduced that some noncanonical HERVs proliferated after the recombination event. Groups were further divided into envelope subgroups (altogether 94) based on sequence similarity and characteristic "immunosuppressive domain" motifs. Intra and inter(super)group, as well as intraclass, recombination involving envelope genes ("env snatching") was a common event. LTR divergence indicated that HERV-K(HML2) and HERVFC had the most recent integrations, HERVL and HUERSP3 the oldest. CONCLUSIONS: A comprehensive HERV classification and characterization approach was undertaken. It should be applicable for classification of all ERVs. Recombination was common among HERV ancestors.

QTREDS: a Ruby on Rails-based platform for omics laboratories.

BACKGROUND: In recent years, the experimental aspects of the laboratory activities have been growing in complexity in terms of amount and diversity of data produced, equipment used, of computer-based workflows needed to process and analyze the raw data generated. To enhance the level of quality control over the laboratory activities and efficiently handle the large amounts of data produced, a Laboratory Management Information System (LIMS) is highly-recommended. A LIMS is a complex software platform that helps researchers to have a complete knowledge of the laboratory activities at each step encouraging them to adopt good laboratory practices. RESULTS: We have designed and implemented Quality and TRacEability Data System--QTREDS, a software platform born to address the specific needs of the CRS4 Sequencing and Genotyping Platform (CSGP). The system written in the Ruby programming language and developed using the Rails framework is based on four main functional blocks: a sample handler, a workflow generator, an inventory management system and a user management system. The wizard-based sample handler allows to manage one or multiple samples at a time, tracking the path of each sample and providing a full chain of custody. The workflow generator encapsulates a user-friendly JavaScript-based visual tool that allows users to design customized workflows even for those without a technical background. With the inventory management system, reagents, laboratory glassware and consumables can be easily added through their barcodes and minimum stock levels can be controlled to avoid shortages of essential laboratory supplies. QTREDS provides a system for privileges management and authorizations to create different user roles, each with a well-defined access profile. CONCLUSIONS: Tracking and monitoring all the phases of the laboratory activities can help to identify and troubleshoot problems more quickly, reducing the risk of process failures and their related costs. QTREDS was designed to address the specific needs of the CSGP laboratory, where it has been successfully used for over a year, but thanks to its flexibility it can be easily adapted to other "omics" laboratories. The software is freely available for academic users from http://qtreds.crs4.it.

MMsINC: a large-scale chemoinformatics database.

MMsINC (http://mms.dsfarm.unipd.it/MMsINC/search) is a database of non-redundant, richly annotated and biomedically relevant chemical structures. A primary goal of MMsINC is to guarantee the highest quality and the uniqueness of each entry. MMsINC then adds value to these entries by including the analysis of crucial chemical properties, such as ionization and tautomerization processes, and the in silico prediction of 24 important molecular properties in the biochemical profile of each structure. MMsINC is consequently a natural input for different chemoinformatics and virtual screening applications. In addition, MMsINC supports various types of queries, including substructure queries and the novel 'molecular scissoring' query. MMsINC is interfaced with other primary data collectors, such as PubChem, Protein Data Bank (PDB), the Food and Drug Administration database of approved drugs and ZINC.

Industrial-scale proteomics: from liters of plasma to chemically synthesized proteins.

Human blood plasma is a useful source of proteins associated with both health and disease. Analysis of human blood plasma is a challenge due to the large number of peptides and proteins present and the very wide range of concentrations. In order to identify as many proteins as possible for subsequent comparative studies, we developed an industrial-scale (2.5 liter) approach involving sample pooling for the analysis of smaller proteins (M(r) generally < ca. 40 000 and some fragments of very large proteins). Plasma from healthy males was depleted of abundant proteins (albumin and IgG), then smaller proteins and polypeptides were separated into 12 960 fractions by chromatographic techniques. Analysis of proteins and polypeptides was performed by mass spectrometry prior to and after enzymatic digestion. Thousands of peptide identifications were made, permitting the identification of 502 different proteins and polypeptides from a single pool, 405 of which are listed here. The numbers refer to chromatographically separable polypeptide entities present prior to digestion. Combining results from studies with other plasma pools we have identified over 700 different proteins and polypeptides in plasma. Relatively low abundance proteins such as leptin and ghrelin and peptides such as bradykinin, all invisible to two-dimensional gel technology, were clearly identified. Proteins of interest were synthesized by chemical methods for bioassays. We believe that this is the first time that the small proteins in human blood plasma have been separated and analyzed so extensively.

In vitro and in silico processes to identify differentially expressed proteins.

We present an integrated proteomics platform designed for performing differential analyses. Since reproducible results are essential for comparative studies, we explain how we improved reproducibility at every step of our laboratory processes, e.g. by taking advantage of the powerful laboratory information management system we developed. The differential capacity of our platform is validated by detecting known markers in a real sample and by a spiking experiment. We introduce an innovative two-dimensional (2-D) plot for displaying identification results combined with chromatographic data. This 2-D plot is very convenient for detecting differential proteins. We also adapt standard multivariate statistical techniques to show that peptide identification scores can be used for reliable and sensitive differential studies. The interest of the protein separation approach we generally apply is justified by numerous statistics, complemented by a comparison with a simple shotgun analysis performed on a small volume sample. By introducing an automatic integration step after mass spectrometry data identification, we are able to search numerous databases systematically, including the human genome and expressed sequence tags. Finally, we explain how rigorous data processing can be combined with the work of human experts to set high quality standards, and hence obtain reliable (false positive < 0.35%) and nonredundant protein identifications.